Title: Linear Model Evaluation with Randomized Residuals in a
Permutation Procedure Description: Linear model calculations are made for many random versions of data.
Using residual randomization in a permutation procedure, sums of squares are
calculated over many permutations to generate empirical probability distributions
for evaluating model effects. This method is described by
Collyer, Sekora, & Adams (2015) <doi:10.1038/hdy.2014.75>. Additionally, coefficients, statistics, fitted values, and residuals generated over many
permutations can be used for various procedures including pairwise tests, prediction, classification, and
model comparison. This package should provide most tools one could need for the analysis of
high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well. Author: Michael Collyer, Dean Adams Maintainer: Michael Collyer <mlcollyer@gmail.com>

Title: eBird Data Extraction and Processing with AWK Description: Extract and process bird sightings records from eBird
(<http://ebird.org>), an online tool for recording bird observations.
Public access to the full eBird database is via the eBird Basic Dataset
(EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable
text file. This package is an interface to AWK for extracting data from the
EBD based on taxonomic, spatial, or temporal filters, to produce a
manageable file size that can be imported into R. Author: Matthew Strimas-Mackey [aut, cre]
(<https://orcid.org/0000-0001-8929-7776>),
Eliot Miller [aut],
Wesley Hochachka [aut],
Cornell Lab of Ornithology [cph] Maintainer: Matthew Strimas-Mackey <mes335@cornell.edu>

Title: Text Cleaning Tools Description: Tools to clean and process text. Tools are geared at checking for substrings that
are not optimal for analysis and replacing or removing them (normalizing) with more
analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
(2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
example, emoticons are often used in text but not always easily handled by analysis
algorithms. The replace_emoticon() function replaces emoticons with word
equivalents. Author: Tyler Rinker [aut, cre],
ctwheels StackOverflow [ctb] Maintainer: Tyler Rinker <tyler.rinker@gmail.com>

Title: 'sf'-Compatible Interface to 'Google Maps' APIs Description: Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds. Author: Michael Dorman [aut, cre],
Tom Buckley [ctb] Maintainer: Michael Dorman <dorman@post.bgu.ac.il>

Title: Manipulation of Microsoft Word and PowerPoint Documents Description: Access and manipulate 'Microsoft Word' and 'Microsoft PowerPoint' documents from R.
The package focuses on tabular and graphical reporting from R; it also provides two functions
that let users get document content into data objects. A set of functions
lets add and remove images, tables and paragraphs of text in new or existing documents.
When working with 'PowerPoint' presentations, slides can be added or removed; shapes inside
slides can also be added or removed. When working with 'Word' documents, a cursor can be
used to help insert or delete content at a specific location in the document. The package
does not require any installation of Microsoft products to be able to write Microsoft files. Author: David Gohel [aut, cre],
Frank Hangler [ctb] (function body_replace_all_text),
Liz Sander [ctb] (several documentation fixes),
Jon Calder [ctb] (update vignettes),
John Harrold [ctb] (fuction annotate_base) Maintainer: David Gohel <david.gohel@ardata.fr>

Title: Stack and Reshape Datasets After Splitting Concatenated Values Description: Online data collection tools like Google Forms often export
multiple-response questions with data concatenated in cells. The
concat.split (cSplit) family of functions splits such data into separate
cells. The package also includes functions to stack groups of columns and
to reshape wide data, even when the data are "unbalanced"---something
which reshape (from base R) does not handle, and which melt and dcast from
reshape2 do not easily handle. Author: Ananda Mahto Maintainer: Ananda Mahto <mrdwab@gmail.com>

Title: Seamless R and C++ Integration Description: The 'Rcpp' package provides R functions as well as C++ classes which
offer a seamless integration of R and C++. Many R data types and objects can be
mapped back and forth to C++ equivalents which facilitates both writing of new
code as well as easier integration of third-party libraries. Documentation
about 'Rcpp' is provided by several vignettes included in this package, via the
'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and
Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013,
<doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018,
<doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details. Author: Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou,
Nathan Russell, Douglas Bates and John Chambers Maintainer: Dirk Eddelbuettel <edd@debian.org>

Title: Unified Parallel and Distributed Processing in R for Everyone Description: The purpose of this package is to provide a lightweight and
unified Future API for sequential and parallel processing of R
expression via futures. The simplest way to evaluate an expression
in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
This package implements sequential, multicore, multisession, and
cluster futures. With these, R expressions can be evaluated on the
local machine, in parallel a set of local machines, or distributed
on a mix of local and remote machines.
Extensions to this package implement additional backends for
processing futures via compute cluster schedulers etc.
Because of its unified API, there is no need to modify any code in order
switch from sequential on the local machine to, say, distributed
processing on a remote compute cluster.
Another strength of this package is that global variables and functions
are automatically identified and exported as needed, making it
straightforward to tweak existing code to make use of futures. Author: Henrik Bengtsson [aut, cre, cph] Maintainer: Henrik Bengtsson <henrikb@braju.com>

Title: Dataframe Difference Tool Description: Functions for comparing two data.frames against
each other. The core functionality is to provide a detailed breakdown of any differences
between two data.frames as well as providing utility functions to help narrow down the
source of problems and differences. Author: Craig Gower [cre, aut],
Kieran Martin [aut] Maintainer: Craig Gower <craig.gower@roche.com>

Title: Copula-Based Estimation and Statistical Process Control for
Serially Correlated Time Series Description: Estimation and statistical process control are performed under
copula-based time-series models.
Available are statistical methods in Long and Emura (2014 JCSA),
Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>,
and Chen and Emura (2018-, submitted). Author: Takeshi Emura, Weiru Chen and Ting-Hsuan Long Maintainer: Takeshi Emura <takeshiemura@gmail.com>

Title: Functions and Datasets for the Book by Keon-Woong Moon Description: Several analysis-related functions for the book entitled "R
statistics and graph for medical articles" (written in Korean), version 1,
by Keon-Woong Moon with Korean demographic data with several plot
functions. Author: Keon-Woong Moon [aut, cre] Maintainer: Keon-Woong Moon <cardiomoon@gmail.com>

Title: Analysis of Data with Mixed Measurement Error and
Misclassification in Covariates Description: Implementation of the augmented
Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
for analyzing the data with mixed measurement error and misclassification. The main
function provides a similar summary output as that of glm() function. Both parametric and
empirical SIMEX are considered in the package. Author: Qihuang Zhang <qihuang.zhang@uwaterloo.ca>, Grace Y. Yi <yyi@uwaterloo.ca> Maintainer: Qihuang Zhang <qihuang.zhang@uwaterloo.ca>